In traditional facial identification processes of identifying one person from an image database of multiple persons in 2D facial recognition, a face photo of the person is compared (or called “matching”) with all face photos in the database using a recognition algorithm or recognition software. The 2D facial recognition algorithm or software will generate a confidence percentage for each matching photo in the database on how much similarity the 2D facial recognition algorithm finds between the face photo of the person and the matching photo in the database. The match of the highest confidence percentage or several matches with top confidence percentages are returned for the operator to make a judgment as to whether there are matches.
Traditional 2D facial recognition algorithms, however, normally have problems identifying face photos because of a large number of variables, such as the lighting present at the time a particular photo is taken or the angle at which the photo is taken, with respect to the face photo being taken of the person and the face photos in the database. Other such variables would be apparent to those skilled in the art. These variables can cause the confidence percentages generated by the 2d facial recognition algorithm or software to be artificially low.